Camera System Research Articles

Comparison of experimental measurements and fast Monte Carlo simulations for typical set-ups in fluoroscopically-guided interventional procedures

PyMCGPU-IR is an application based on the Monte Carlo code MCGPU-IR that automatically retrieves procedure information from X-ray systems and medical worker positions from a tracking camera system. PyMCGPU-IR calculates personal dose equivalent values, doses in organs and effective dose values for both patient and medical staff in interventional radiology procedures and displays them visually. The code's main advantage lies in its time efficiency, enabling simulations in under 2 min with statistical uncertainties below 5% (k = 2).This study involved testing in a hospital room using an interventional X-ray system. A RANDO phantom simulated the patient, with passive dosimeters affixed to the back for measuring skin dose values. A PMMA slab phantom represented the operator, with passive and active dosimeters affixed to its front surface to measure Hp(10). The irradiation conditions were simulated with PyMCGPU-IR using voxelized geometries to represent both phantoms. Results demonstrate good agreement between PyMCGPU-IR simulations and measured patient skin dose values, with differences of up to 5% for mean skin dose and up to 14% for the peak skin dose. Concerning Hp(10) values on the operator phantom, PyMCGPU-IR calculated values fall within the uncertainty ranges of dosimeter measurements for most points. The highest Hp(10) discrepancy is 42%, which is acceptable when compared with the typical variability observed between active and passive personal dosimeters measurements in interventional radiology. The results demonstrate PyMCGPU-IR's satisfactory performance for patient and personal dosimetry, compared to existing solutions like commercial skin dose calculation software and personal physical dosimeters.

Drone-based photogrammetry combined with deep learning to estimate hail size distributions and melting of hail on the ground

Abstract. Hail is a major threat associated with severe thunderstorms, and estimating the hail size is important for issuing warnings to the public. For the validation of existing operational, radar-derived hail estimates, ground-based observations are necessary. Automatic hail sensors, for example within the Swiss Hail Network, record the kinetic energy of hailstones to estimate the hail sizes. Due to the small size of the observational area of these sensors (0.2 m2), the full hail size distribution (HSD) cannot be retrieved. To address this issue, we apply a state-of-the-art custom trained deep learning object detection model to drone-based aerial photogrammetric data to identify hailstones and estimate the HSD. Photogrammetric data of hail on the ground were collected for one supercell thunderstorm crossing central Switzerland from southwest to northeast in the afternoon of 20 June 2021. The hail swath of this intense right-moving supercell was intercepted a few minutes after the passage at a soccer field near Entlebuch (canton of Lucerne, Switzerland) and aerial images were taken by a commercial DJI drone, equipped with a 45-megapixel full-frame camera system. The resulting images have a ground sampling distance (GSD) of 1.5 mm per pixel, defined by the focal length of 35 mm of the camera and a flight altitude of 12 m above the ground. A 2-dimensional orthomosaic model of the survey area (750.4 m2) is created based on 116 captured images during the first drone mapping flight. Hail is then detected using a region-based convolutional neural network (Mask R-CNN). We first characterize the hail sizes based on the individual hail segmentation masks resulting from the model detections and investigate the performance using manual hail annotations by experts to generate validation and test data sets. The final HSD, composed of 18 207 hailstones, is compared with nearby automatic hail sensor observations, the operational weather-radar-based hail product MESHS (Maximum Expected Severe Hail Size) and crowdsourced hail reports. Based on the retrieved data set, a statistical assessment of sampling errors of hail sensors is carried out. Furthermore, five repetitions of the drone-based photogrammetry mission within 18.65 min facilitate investigations into the hail-melting process on the ground.

The PMT system of the TRIDENT pathfinder experiment

Next generation neutrino telescopes are highly anticipated to boost the development of neutrino astronomy. A multi-cubic-kilometer neutrino telescope, TRopIcal DEep-sea Neutrino Telescope (TRIDENT), was proposed to be built in the South China Sea. The detector aims to achieve ∼ 0.1 degree angular resolution for track-like events at energy above 100 TeV by using hybrid digital optical modules, opening new opportunities for neutrino astronomy. In order to measure the water optical properties and marine environment of the proposed TRIDENT site, a pathfinder experiment was conducted, in which a 100-meter-long string consisting of three optical modules was deployed at a depth of 3420 m to perform in-situ measurements. The central module emits light by housing LEDs, whereas the other two modules detect light with two independent and complementary systems: the PMT and the camera systems. By counting the number of detected photons and analyzing the photon arrival time distribution, the PMT system can measure the absorption and scattering lengths of sea water, which serve as the basic inputs for designing the neutrino telescope. In this paper, we present the design concept, calibration and performance of the PMT system in the pathfinder experiment.

An Empirical Calibration of the Tip of the Red Giant Branch Distance Method in the Near Infrared. I. Hubble Space Telescope WFC3/IR F110W and F160W Filters

The tip of the red giant branch (TRGB) based distance method in the I band is one of the most efficient and precise techniques for measuring distances to nearby galaxies (D ≲ 15 Mpc). The TRGB in the near-infrared (NIR) is 1–2 mag brighter relative to the I band, and has the potential to expand the range over which distance measurements to nearby galaxies are feasible. Using Hubble Space Telescope (HST) imaging of 12 fields in eight nearby galaxies, we determine color-based corrections and zero-points of the TRGB in the Wide Field Camera 3 IR (WFC3/IR) F110W and F160W filters. First, we measure TRGB distances in the I band equivalent Advanced Camera System (ACS) F814W filter from resolved stellar populations with the HST. The TRGB in the ACS F814W filter is used for our distance anchor and to place the WFC3/IR magnitudes on an absolute scale. We then determine the color dependence (a proxy for metallicity/age) and zero-point of the NIR TRGB from photometry of WFC3/IR fields that overlap with the ACS fields. The new calibration is accurate to ∼1% in distance relative to the F814W TRGB. Validating the accuracy of the calibrations, we find that the distance modulus for each field using the NIR TRGB calibration agrees with the distance modulus of the same field as determined from the F814W TRGB. This is a JWST preparatory program, and the work done here will directly inform our approach to calibrating the TRGB in JWST NIRCam and NIRISS photometric filters.

Biochar as a slag foaming agent in EAF – A novel experimental setup

Slag foaming practice is employed widely in electric arc furnace steelmaking to improve the energy efficiency, protect the furnace structures and reduce noise pollution. Slag foaming is typically launched by injecting fossil-based carbonaceous material (e.g. coke dust) into the melt. In this study, a novel laboratory-scale experimental setup was used for studying the substitution of fossil carbon by biochar as slag foaming agent. The setup was equipped with an injection device for feeding carbon and a camera system for observing the foaming phenomenon and recording the process. A set of experiments was conducted for studying the foaming in slag-carbon systems using two carbonaceous materials: 1) coke dust was used as a reference material and 2) a high-quality biochar was used as a possible replacement. In the experiments, sufficient foaming was achieved with both of the carbonaceous materials. The biochar produced almost equal foaming behavior as coke dust. The results indicate that biochar could be used to substitute carbon for slag foaming in the EAF.

Drought shortens subtropical understory growing season by advancing leaf senescence.

Subtropical forests, recognized for their intricate vertical canopy stratification, exhibit high resistance to extreme drought. However, the response of leaf phenology to drought in the species-rich understory remains poorly understood. In this study, we constructed a digital camera system, amassing over 360,000 images through a 70% throughfall exclusion experiment, to explore the drought response of understory leaf phenology. The results revealed a significant advancement in understory leaf senescence phenology under drought, with 11.75 and 15.76 days for the start and end of the leaf-falling event, respectively. Pre-season temperature primarily regulated leaf development phenology, whereas soil water dominated the variability in leaf senescence phenology. Under drought conditions, temperature sensitivities for the end of leaf emergence decreased from -13.72 to -11.06 days °C-1, with insignificance observed for the start of leaf emergence. Consequently, drought treatment shortened both the length of the growing season (15.69 days) and the peak growth season (9.80 days) for understory plants. Moreover, this study identified diverse responses among intraspecies and interspecies to drought, particularly during the leaf development phase. These findings underscore the pivotal role of water availability in shaping understory phenology patterns, especially in subtropical forests.

Onboard Surveillance Camera Robotic Vehicle

Abstract: Surveillance cameras have been integrated with autonomous vehicles for various purposes like monitoring, guarding and spying. This research paper focuses on how to design, implement, deploy and evaluate a robotic vehicle with a surveillance camera system. This study is on both hardware and software architecture, choice of camera, image processing techniques as wellas communication protocol with other devices. Therefore, the suggested system aims at improving surveillance in addition with the number of sensors which can prevent any accident and can increase remote monitoring. The blynk server is used for the transmission of the data of various servers used in the system. Telegram bot is used to perform a two way communication with the system.

Predicting the Therapeutic Results of Unwanted Hair Growth with the Alexandrite Laser Device

The growth of excess hair on the chin, under the neck, near the navel, around the chest, etc. is one of the main disorders that both women and men face. The best, most efficient and least invasive way to remove thick and dark hair is to use a laser. It is important to check the effectiveness of laser in removing unwanted hair after treatment sessions. In this study, the treatment of unwanted hair in the armpit or thigh area in 14 patients was investigated. The participants with skin type I and III were aged 27-37 years and were treated with 755 nm alexandrite laser. Examination of each area (armpit and thigh) was done with a camera system specifically to count the number of hairs at 1 and 3 months after the treatment period. In this study, Monte Carlo modeling was used to estimate the density of excess hair remaining after treatment sessions, and the results showed that the number of excess hair decreased both by Monte Carlo method and manual counting method, and the accuracy rate of the simulation was 75%.

Time-gated single-pixel imaging of Cherenkov emission from a medical linear accelerator.

Cherenkov imaging is an ideal tool for real-time in vivo verification of a radiation therapy dose. Given that radiation is pulsed from a medical linear accelerator (LINAC) together with weak Cherenkov emissions, time-gated high-sensitivity imaging is required for robust measurements. Instead of using an expensive camera system with limited efficiency of detection in each pixel, a single-pixel imaging (SPI) approach that maintains promising sensitivity over the entire spectral band could be used to provide a low-cost and viable alternative. A prototype SPI system was developed and demonstrated here in Cherenkov imaging of LINAC dose delivery to a water tank. Validation experiments were performed using four regular fields and an intensity-modulated radiotherapy (IMRT) delivery plan. The Cherenkov image-based projection percent depth dose curves (pPDDs) were compared to pPDDs simulated by the treatment planning system (TPS), with an overall average error of 0.48, 0.42, 0.65, and 1.08% for the 3, 5, 7, and 9 cm square beams, respectively. The composite image of the IMRT plan achieved a 85.9% pass rate using 3%/3 mm gamma index criteria, in comparing Cherenkov intensity and TPS dose. This study validates the feasibility of applying SPI to the Cherenkov imaging of radiotherapy dose for the first time to our knowledge.

Towards robotic disassembly: A comparison of coarse-to-fine and multimodal fusion screw detection methods

To accelerate the transition towards a more circular economy it is believed essential to increase the economic viability and hence the adoption of de- and remanufacturing processes. For this, industry 4.0 technologies, including robotization, computer vision and artificial intelligence, are commonly considered as the main enablers to reduce costs while boosting performances of de- and remanufacturing processes. However, the essential step in de- and remanufacturing of screw detection for robotic disassembly is still confronted with various challenges, such as false negatives due to deep screw holes and false positives caused by similar patterns and colors, e.g. letters in labels. Previous studies improved the detection accuracy by capturing images of only a fine part of the product and neglected the negative impact of this imaging approach on the overall time efficiency of the disassembly process. Therefore, two of the most distinct approaches are evaluated in the presented research: 1. A Coarse-to-Fine detection method, where potential screws across the entire product are detected by an area camera in the coarse stage to generate an optimal unscrewing path and screw types and locations are further refined in a second instance, the fine stage. 2. A multimodal fusion detection method, where a combination of a high-resolution color line and 3D triangulation line camera system captures full-product images allowing a single run screw detection in the RGB, Intensity and Depth (RGBID) images. Furthermore, a pooling-based aligned fusion module (PAlign) is introduced to improve the feature representation and mitigate the encountered spatial misalignment of different modality branches. Two industrially representative datasets comprising images of laptops and hard disk drives are collected for the training and evaluation of the screw detection and unscrewing task. Experimental results show that the RGBID fusion method with PAlign module achieves an unscrewing success rate of 80.4 % with an average detection and unscrewing speed of 2.27 s per screw, which is almost double the speed when compared to the Coarse-to-Fine approach. Through a detailed analysis comparing these most distinct screw detection methods in terms of imaging approach, lighting and camera hardware (1/2, area/line, low/high resolution, RGB/RGBID, 600/25.000€), this study provides concrete insights and recommendations aimed at enhancing the efficiency of disassembly systems for sustainable demanufacturing.

Evaluation of alternate image capturing devices for recording fingermarks at crime scenes

ABSTRACT This project evaluated the fingermark imaging ability of several alternate image capturing devices selected for their potential to assist with the recording of fingermarks in difficult-to-reach locations commonly encountered at crime scenes, e.g. the inside surface of door handles. Four small form-factor non-DSLR camera systems — intraoral camera, borescope, fibre-optic camera and an iPhone — were evaluated under controlled conditions for their ability to image fingermarks with sufficient detail for identification. Evaluation was conducted using black powdered fingermarks and standard photographic test targets. A qualitative visual evaluation approach was also developed to gauge whether image quality was appropriate for fingermark identification. The results obtained indicated that most of the systems were capable of capturing fingermark details suitable for identification in controlled environments; however, the inherent contrast of the fingermark had an impact on the success of various imaging devices. The practicality and capability of alternate camera devices for fingermark recovery in pseudo-operational conditions is part of ongoing research.

Real‐Time Noise Source Estimation of a Camera System from an Image and Metadata

Autonomous machines must self‐maintain proper functionality to ensure the safety of humans and themselves. This pertains particularly to its cameras as predominant sensors to perceive the environment and support actions. A fundamental camera problem addressed in this study is noise. Solutions often focus on denoising images a posteriori, that is, fighting symptoms rather than root causes. However, tackling root causes requires identifying the noise sources, considering the limitations of mobile platforms. In this work, a real‐time, memory‐efficient, and reliable noise source estimator that combines data‐based and physically based models is investigated. To this end, a deep neural network that examines an image with camera metadata for major camera noise sources is built and trained. In addition, it quantifies unexpected factors that impact image noise or metadata. This study investigates seven different estimators on six datasets that include synthetic noise, real‐world noise from two camera systems, and real‐field campaigns. For these, only the model with most metadata is capable to accurately and robustly quantify all individual noise contributions. This method outperforms total image noise estimators and can be plug‐and‐play deployed. It also serves as a basis to include more advanced noise sources, or as part of an automatic countermeasure feedback loop to approach fully reliable machines.

Optimal design and experiments of a novel bobbin thread-hooking mechanism with RRSC (revolute–revolute–spherical–cylindrical) spatial four-bar linkage

Abstract. To solve the problem of low supply of bobbin thread and frequent bobbin changes existing in current embroidery machines, a novel bobbin thread-hooking mechanism with an RRSC (revolute–revolute–spherical–cylindrical) spatial four-bar linkage is proposed which can achieve a large number and continuous supply of bobbin thread. Based on the analysis of the chain stitch formation principle, the design requirements for motion trajectory and posture of the hooking mechanism are proposed. The methods of direction cosine matrix and mechanism identity condition are applied in kinematics modeling and analysis of the RRSC mechanism. The optimization design model and computer-aided software are developed, then the human–computer interaction optimization method is used to obtain optimal solutions to meet working requirements. The physical prototype is machined and a test bench is built. The virtual simulation and prototype high-speed camera kinematics tests are conducted. The results verify the correctness of mechanism design and show good application feasibility of the mechanism.

De[formula omitted]Net: Under-display camera image restoration with feature deconvolution and kernel decomposition

While the under-display camera (UDC) system provides an effective solution for notch-free full-screen displays, it inevitably causes severe image quality degradation due to the diffraction phenomenon. Recent methods have achieved decent performance with deep neural networks, yet the characteristic of the point spread function (PSF) is less studied. In this paper, considering the large support and spatial inconsistency of PSF, we propose De2Net for UDC image restoration with feature deconvolution and kernel decomposition. In terms of feature deconvolution, we introduce Wiener deconvolution as a preliminary process, which alleviates feature entanglement caused by the large PSF support. Besides, the deconvolution kernel can be learned from training images, eliminating the tedious PSF-obtaining process. As for kernel decomposition, we observe regular patterns for PSFs at different positions. Thus, with a kernel prediction network (KPN) deployed for handling the spatial inconsistency problem, we improve it from two aspects, i.e., (i) decomposing the predicted kernels into a set of bases and weights, (ii) decomposing kernels into groups with different dilation rates. These modifications largely improve the receptive field under certain memory limits. Extensive experiments on three commonly used UDC datasets show that De2Net outperforms existing methods both quantitatively and qualitatively. Source code and pre-trained models are available at https://github.com/HyZhu39/De2Net.

Auroral breakup detection in all-sky images by unsupervised learning

Abstract. Due to a large number of automatic auroral camera systems on the ground, image data analysis requires more efficiency than what human expert visual inspection can provide. Furthermore, there is no solid consensus on how many different types or shapes exist in auroral displays. We report the first attempt to classify auroral morphological forms by an unsupervised learning method on an image set that contains both nightside and dayside aurora. We used 6 months of full-colour auroral all-sky images captured at a high-Arctic observatory on Svalbard, Norway, in 2019–2020. The selection of images containing aurora was performed manually. These images were then input into a convolutional neural network called SimCLR for feature extraction. The clustered and fused features resulted in 37 auroral morphological clusters. In the clustering of auroral image data with two different time resolutions, we found that the occurrence of 8 clusters strongly increased when the image cadence was high (24 s), while the occurrence of 14 clusters experienced little or no change with changes in input image cadence. We therefore investigated the temporal evolution of a group of eight “active aurora” clusters. Time periods for which this active aurora persisted for longer than two consecutive images with a maximum cadence of 6 min coincided with ground-magnetic deflections, and their occurrence was found to maximize around magnetic midnight. The active aurora onsets typically included vortical auroral structures and equivalent current patterns typical for substorms. Our findings therefore suggest that our unsupervised image clustering method can be used to detect auroral breakups in ground-based image datasets with a temporal accuracy determined by the image cadence.

High-speed camera system for efficient monitoring of invasive plant species along roadways.

Invasive plant species pose ecological threats to native ecosystems, particularly in areas adjacent to roadways, considering that roadways represent lengthy corridors through which invasive species can propagate. Traditional manual survey methods for monitoring invasive plants are labor-intensive and limited in coverage. This paper introduces a high-speed camera system, named CamAlien, designed to be mounted on vehicles for efficient invasive plant species monitoring along roadways. The camera system captures high-quality images at rapid intervals, to monitor the full roadside when following traffic speed. The system utilizes a global shutter sensor to reduce distortion and geotagging for precise localistion. The camera system makes it possible to collect extensive data sets, which can be used for a digital library of the invasive species and their locations, but also subsequent training of machine learning algorithms for automated species recognition.

Solar Wind Charge-Exchange X-ray Emissions from the O5+ Ions in the Earth’s Magnetosheath

The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. Solar wind O6+ ions, which are the primary charge state for oxygen ions in solar wind, are considered. The line emissivity of the charge-exchange-borne O5+ ions is calculated by the Spectral Analysis System for Astrophysical and Laboratory (SASAL). It is found that the emission lines from O5+ range from 105.607 to 118.291 eV with a strong line at 107.047 eV. We then simulate the magnetosheath X-ray emission intensity distributions with a virtual camera at two positions of the north pole and dusk at six stages during the passing of a perpendicular interplanetary shock combined with a tangential discontinuity structure through the Earth’s magnetosphere. During this process, the X-ray emission intensity increases with time, and the maximum value is 27.11 keV cm−2 s−1 sr−1 on the dayside, which is 4.5 times that before the solar wind structure reached the Earth. A clear shock structure can be seen in the magnetosheath and moves earthward. The maximum emission intensity seen at dusk is always higher than that seen at the north pole.

Design and Implementation of a Low-Cost, Linear Robotic Camera System, Targeting Greenhouse Plant Growth Monitoring

Automated greenhouse production systems frequently employ non-destructive techniques, such as computer vision-based methods, to accurately measure plant physiological properties and monitor crop growth. By utilizing an automated image acquisition and analysis system, it becomes possible to swiftly assess the growth and health of plants throughout their entire lifecycle. This valuable information can be utilized by growers, farmers, and crop researchers who are interested in self-cultivation procedures. At the same time, such a system can alleviate the burden of daily plant photography for human photographers and crop researchers, while facilitating automated plant image acquisition for crop status monitoring. Given these considerations, the aim of this study was to develop an experimental, low-cost, 1-DOF linear robotic camera system specifically designed for automated plant photography. As an initial evaluation of the proposed system, which targets future research endeavors of simplifying the process of plant growth monitoring in a small greenhouse, the experimental setup and precise plant identification and localization are demonstrated in this work through an application on lettuce plants, imaged mostly under laboratory conditions.

Internet of Things assisted Unmanned Aerial Vehicle for Pest Detection with Optimized Deep Learning Model

IoT technologies & UAVs are frequently utilized in ecological monitoring areas. Unmanned Aerial Vehicles (UAVs) & IoT in farming technology can evaluate crop disease & pest incidence from the ground’s micro & macro aspects, correspondingly. UAVs could capture images of farms using a spectral camera system, and these images are been used to examine the presence of agricultural pests and diseases. In this research work, a novel IoT- assisted UAV- based pest detection with Arithmetic Crossover based Black Widow Optimization-Convolutional Neural Network (ACBWO-CNN) model is developed in the field of agriculture. Cloud computing mechanism is used for monitoring and discovering the pest during crop production by using UAVs. The need for this method is to provide data centers, so there is a necessary amount of memory storage in addition to the processing of several images. Initially, the collected input image by the UAV is assumed on handling the via-IoT-cloud server, from which the pest identification takes place. The pest detection unit will be designed with three major phases: (a) background &foreground Segmentation, (b) Feature Extraction & (c) Classification. In the foreground and background Segmentation phase, the K-means clustering will be utilized for segmenting the pest images. From the segmented images, it extracts the features including Local Binary Pattern (LBP) &improved Local Vector Pattern (LVP) features. With these features, the optimized CNN classifier in the classification phase will be trained for the identification of pests in crops. Since the final detection outcome is from the Convolutional Neural Network (CNN); its weights are fine-tuned through the ACBWO approach. Thus, the output from optimized CNN will portray the type of pest identified in the field. This method’s performance is compared to other existing methods concerning a few measures.

Development of dynamic centrifuge models for measurement and visualization of deformation mechanisms in liquefiable soils

Recent advancements in geotechnical physical modeling have enabled visualization and analysis of deformation mechanisms in soil sections through the integration of Particle Image Velocimetry (PIV) in dynamic centrifuge modeling. PIV requires a rigid wall container. In this paper, we summarize the design considerations that enable reliable measurement and visualization of soil deformation mechanisms at the University of Colorado (CU) Boulder’s 400 g-ton, 5.5 m radius centrifuge facility. The primary objective of this system is to investigate the response of complex and stratigraphically variable, saturated granular soil deposits beneath shallow-founded structures, though it can also be used for other configurations that benefit from advanced visualization in the centrifuge. The setup aims to enable quantification and visualization of different deformation mechanisms, including shear, volumetric, and soil ejecta formation, near and away from structures. The paper provides detailed design considerations for the system components, including a rigid container with a transparent Perspex wall and duct seal inclusions, a linear-elastic single-degree of freedom (SDOF) structure, soil texture, a potential ground improvement technique, and a high-speed camera system. Through fully-coupled, three-dimensional (3D) nonlinear finite element analyses, we investigate the influence of container type, domain size, and duct seal geometry on boundary effects, with consideration of nonlinearities within various soil profile configurations with and without the presence of a building model. The findings highlight the critical impact of proximity to lateral boundaries of the container on accelerations, excess pore pressures, foundation settlement, tilt, and shear strains, as well as the benefits of duct seal in reducing those boundary effects. The results also show that the extent of boundary effects on key performance measures depends on the properties of the container, soil layers, and ground motion. The simulations also show that surface ejecta potential within stratigraphic soil profiles is highly sensitive to the depth of the groundwater table and variations in soil permeability, which must be considered in designing experimental programs that investigate the development of soil ejecta.